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Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning

Created by
  • Haebom

Author

Hugues Van Assel, Mark Ibrahim, Tommaso Biancalani, Aviv Regev, Randall Balestriero

Outline

This paper compares and analyzes two major paradigms in self-supervised learning (SSL): reconstruction and concatenated embedding. Reconstruction methods focus on recovering original samples in the input space, while concatenated embedding methods align representations of different views in the latent space. This study elucidates the core mechanisms of both paradigms and precisely characterizes the impact of the view generation process on learned representations. Furthermore, it demonstrates that both SSL paradigms require minimal alignment conditions between irrelevant features and augmentation. When the size of irrelevant features is large, concatenated embedding methods are more suitable than reconstruction-based methods because they require weaker alignment conditions.

Takeaways, Limitations

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Takeaways:
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We clearly present the core mechanisms of reconfiguration and concatenative embeddings, two major paradigms of self-supervised learning (SSL).
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We quantitatively analyze the impact of the view generation (data augmentation) process on the learned representation.
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We demonstrate the superiority of the concatenated embedding method when the number of irrelevant features is large.
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We clearly present the pros and cons of the two paradigms and support the success of the joint embedding method on real-world datasets.
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Limitations:
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It may not include in-depth analysis of specific datasets or real-world use cases.
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The analysis may be limited to a specific type of view creation method (data augmentation).
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Comparisons with other various approaches to SSL may be limited.
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